Kelly, Malamud and Zhou (2024, KMZ) argue that simple models severely understate return predictability relative to complex ones in which the number of predictors vastly exceeds the number of training window observations. KMZ prove that under certain conditions expected out-of-sample forecast accuracy and portfolio performance are strictly increasing in model complexity. They call this the ‘Virtue of Complexity’ (VoC). I show that KMZ’s empirical VoC results are the consequence of two implementation choices: (1) a zero-intercept restriction imposed on the prediction models, and (2) an unconventional aggregation scheme used to construct performance measures for the machine learning models. Both of these choices artificially worsen the performance of KMZ’s ‘simple’ models and lead to absurd portfolio performance outcomes such as negative Sharpe ratios with corresponding positive expected returns. Using a simulation experiment, I show that equivalent VoC results can be obtained from artificially generated and thus unpredictable i.i.d. returns data. Overall, the performance of KMZ’s complex models is disappointing. Standard linear models estimated with an intercept term generate Sharpe ratios that are up to 40% larger than from KMZ’s complex models.